class BasicTest(unittest.TestCase):
def setUp(self):
bl = generate_block_for_sorting(
nb_unit=3,
duration=1. * pq.s,
noise_ratio=0.2,
nb_segment=2,
)
rcg = bl.recordingchannelgroups[0]
self.sps = SpikeSorter(rcg, initial_state='full_band_signal')
def tearDown(self):
pass
def test_getattr_aliases(self):
self.assertIs(self.sps.segs, self.sps.segments)
self.assertRaises(AttributeError, getattr, self.sps,
'i_love_my_mother')
def test_getattr_runstep(self):
self.sps.ButterworthFilter(f_low=200.)
self.assertIsInstance(self.sps.history[-1]['methodInstance'],
ButterworthFilter)
def test_one_standart_pipeline(self):
self.sps.ButterworthFilter(f_low=200.)
self.assertIsNotNone(self.sps.filtered_sigs)
self.sps.MedianThresholdDetection(
sign='-',
median_thresh=6,
)
self.assertIsNotNone(self.sps.spike_index_array)
self.sps.AlignWaveformOnDetection(left_sweep=1 * pq.ms,
right_sweep=2 * pq.ms)
self.assertIsNotNone(self.sps.seg_spike_slices)
self.assertIsNotNone(self.sps.spike_waveforms)
self.assertIsNotNone(self.sps.left_sweep)
self.assertIsNotNone(self.sps.right_sweep)
self.sps.PcaFeature(n_components=3)
self.assertIsNotNone(self.sps.waveform_features)
self.sps.SklearnGaussianMixtureEm(n_cluster=12, n_iter=500)
self.assertIsNotNone(self.sps.spike_clusters)
self.assertIsNotNone(self.sps.cluster_names)
def test_apply_history_to_other(self):
sps2 = SpikeSorter(self.sps.rcg, initial_state='full_band_signal')
self.sps.apply_history_to_other(sps2)
def test1():
bl = generate_block_for_sorting(
nb_unit=6,
duration=10. * pq.s,
noise_ratio=0.2,
nb_segment=2,
)
rcg = bl.recordingchannelgroups[0]
spikesorter = SpikeSorter(rcg)
spikesorter.ButterworthFilter(f_low=200.)
spikesorter.RelativeThresholdDetection(sign='-',
relative_thresh=4.,
noise_estimation='MAD',
threshold_mode='crossing')
#~ spikesorter.RelativeThresholdDetection(sign= '-', relative_thresh = 4.,noise_estimation = 'MAD', threshold_mode = 'peak')
spikesorter.AlignWaveformOnDetection(left_sweep=1 * pq.ms,
right_sweep=2 * pq.ms,
sign='-')
#~ spikesorter.AlignWaveformOnPeak(left_sweep = 1*pq.ms , right_sweep = 2*pq.ms, sign = '-', peak_method = 'biggest_amplitude')
#~ spikesorter.AlignWaveformOnPeak(left_sweep = 1*pq.ms , right_sweep = 2*pq.ms, sign = '-', peak_method = 'closer')
#~ spikesorter.AlignWaveformOnCentralWaveform(left_sweep = 1*pq.ms , right_sweep = 2*pq.ms, )
#~ print spikesorter.spike_waveforms.shape
#~ s0 = spikesorter.spike_waveforms.shape[0]
#~ wf2 = spikesorter.spike_waveforms.reshape(s0, -1)
#~ from matplotlib import pyplot
#~ pyplot.plot(wf2[:-10, :].transpose())
#~ pyplot.show()
print spikesorter
spikesorter.check_display_attributes()
from OpenElectrophy.gui.spikesorting import AverageWaveforms, AllWaveforms
app = QApplication([])
w1 = AverageWaveforms(spikesorter=spikesorter)
w1.refresh()
w1.show()
w2 = AllWaveforms(spikesorter=spikesorter)
w2.refresh()
w2.show()
app.exec_()
def test1():
bl = generate_block_for_sorting(
nb_unit=6,
duration=10. * pq.s,
noise_ratio=0.2,
nb_segment=2,
)
rcg = bl.recordingchannelgroups[0]
spikesorter = SpikeSorter(rcg)
spikesorter.ButterworthFilter(f_low=200.)
#~ spikesorter.RelativeThresholdDetection(sign= '-', relative_thresh = 4.,noise_estimation = 'MAD', threshold_mode = 'crossing')
spikesorter.RelativeThresholdDetection(sign='-',
relative_thresh=4.,
noise_estimation='MAD',
threshold_mode='peak')
spikesorter.AlignWaveformOnDetection(left_sweep=1.5 * pq.ms,
right_sweep=2.5 * pq.ms,
sign='-')
spikesorter.PcaFeature(n_components=4)
spikesorter.SklearnKMeans(n_cluster=3)
#~ spikesorter.AlignWaveformOnPeak(left_sweep = 1*pq.ms , right_sweep = 2*pq.ms, sign = '-', peak_method = 'biggest_amplitude')
#~ spikesorter.AlignWaveformOnPeak(left_sweep = 1*pq.ms , right_sweep = 2*pq.ms, sign = '-', peak_method = 'closer')
spikesorter.AlignWaveformOnCentralWaveform(
left_sweep=1 * pq.ms,
right_sweep=2 * pq.ms,
shift_estimation_method='taylor order1',
#~ shift_estimation_method = 'taylor order2',
#~ shift_estimation_method ='optimize',
#~ shift_method = 'spline',
shift_method='sinc',
max_iter=3)
step = spikesorter.history[-1]
instance = step['methodInstance']
fig = pyplot.figure()
instance.plot_iterative_centers(fig, spikesorter)
pyplot.show()
duration = 10.*pq.s,
noise_ratio = 0.2,
nb_segment = 2,
)
rcg = bl.recordingchannelgroups[0]
spikesorter = SpikeSorter(rcg, initial_state='full_band_signal')
# Apply a chain
spikesorter.ButterworthFilter( f_low = 200.)
spikesorter.MedianThresholdDetection(sign= '-', median_thresh = 6.,)
spikesorter.AlignWaveformOnPeak(left_sweep = 1*pq.ms , right_sweep = 2*pq.ms, sign = '-')
# display widget interactivively
from OpenElectrophy.gui.spikesorting import AverageWaveforms
w1 = AverageWaveforms(spikesorter = spikesorter)
w1.show()
# test another step methods
spikesorter.AlignWaveformOnDetection(left_sweep = 1*pq.ms , right_sweep = 2*pq.ms)
w1.refresh()
